EDUCAUSE Analytics Sprint, Day 1 Recap: What Does Analytics Mean for Higher Education?

EDUCAUSE Analytics Sprint, Day 1 Recap: What Does Analytics Mean for Higher Education?

The EDUCAUSE Analytics Sprint kicked off today, with the theme “What Does Analytics Mean for Higher Education?” A broad question, indeed, reflected in the wide-ranging comments and discussion, both in today’s webinar and in the many side and back channels of communication. Sprint participants shared their thoughts and concerns about issues including:

The quality and selection of data

The costs and benefits of analytics

The environmental factors that facilitate (and are enabled by) an active culture of analytics

Staff skills needed for an effective analytics program

Behind all of this, though, the real takeaway from the day might simply be this: What wouldn’t you give to have Freeman Hrabowski as the president of your institution? Here we have a university president who insists on having the CIO reporting to him, who believes that data are a vital component of the management of the institution and efforts to realize its goals, and who has worked with his staff and the university’s regents to build a climate in which analytics can thrive.

Analytics will never be a one-size-fits-all proposition (as if anything is for higher education), and there are more questions—fundamental questions—than answers. Some of the voices on today’s social networks offered these thoughts:

What is analytics? Is it a process? A tool? A philosophy?

Moving from theory to practice is the hard part.

We need to develop a common language and understanding.

Higher education is data-rich but information-poor, and analytics can help you move from collecting numbers to finding meaning.

Despite the anxiety that comes from the apparent scope of analytics and the absence of a clear path, today’s discussion and the experiences of UMBC—as described by Freeman A. Hrabowski III, president; Jack Suess, vice president of information technology and CIO; and Michael Dillon, director of institutional research—point in some fairly clear directions.

Analytics has to be broad-based. Whether it’s building a bridge between IT and institutional research, addressing faculty concerns about their evaluation, or finding the connections between cost, academic success, and retention, analytics can and must break down walls that separate campus groups that are often highly protective of their turf.

Start with questions. Analytics should solve problems. Rather than looking at numbers and wondering what they might tell you, think about the questions whose answers will improve the functioning of the university and the success of its students. Then find the answers in the data.

See the facets. Analytics can lead to gains in many areas: student success, admissions and recruitment, advising, fundraising, and the efficient use of institutional resources, to name a few. Work to see the many areas that could benefit from an analytics program, and remember that they are interconnected.

Understand, but don’t fear, the money involved. Like everything, analytics costs money, but a culture in which those dollars are seen as an investment rather than an expense is likely to produce more and better results from analytics efforts.

Not every university president is as engaged with IT and analytics as Freeman Hrabowski, but analytics surely has something to offer every institutional leader—increased graduation rates, higher overall student performance, greater organizational efficiency, and other benefits. As pressures rise for accountability in higher education, the potential benefits of analytics also increase. The logistics and policies are difficult, and analytics is not a silver bullet. But done thoughtfully and earnestly, analytics holds enormous promise.